Finding Attributes from Candidates Using HowNet

Existing studies about domain knowledge in ontology learning and knowledge acquisition mostly focus on finding concepts and is-a relations among them. However, attribute that is important for the understanding of concepts has received little attention. Thus, this paper proposes an approach for finding attributes from candidates using attribute sememe knowledge defined in HowNet. Candidate attributes collected from texts are subdivided into three types: non-attribute vocabularies, invalid attribute, and valid attribute. Non-attribute vocabularies are firstly filtered out from the candidates using the taxonomic knowledge of attribute in HowNet and similarity measures between the candidate and existing known attribute in HowNet. Then invalid attributes are then discarded from the rest of candidates using the attribute-host knowledge between the attribute and its host concept in HowNet. Further, experimental studies show a promising result.